上升的自身感知通路的深度学习模型受到错觉的影响
Adriana Perez Rotondo1,2, Merkourios Simos1,2, Florian David1,2
1Brain Mind Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
Experimental physiology
|June 4, 2025
概括
任务驱动的自觉模型可以复制肌振动引起的经典幻觉. 这些模型表明,单独的初级附带因素就足以解释这些感官偏差,为身体状态估计提供了洞察力.
科学领域:
- 神经科学是一个神经科学.
- 计算神经科学是一种神经科学.
- 感官感知是一种感官感知.
背景情况:
- 自身感知,身体位置和运动的感觉,对于感知和行动至关重要.
- 相似于视觉错觉的自身感知错觉,可以改变对身体状态的感知.
- 已知肌振动会诱导自感估计中的偏差.
研究的目的:
- 用计算方法建模经典的自感幻觉.
- 为了研究肌肉 afferents 在产生这些幻觉的作用.
- 为了确定任务驱动型模型是否可能表现出对自感幻觉的易感性.
主要方法:
- 开发任务驱动的计算模型,通过训练来从肌肉输入 (初级和二级 afferents) 推断身体状态.
- 模拟肌振动,通过分析它们对初级附属体的影响,而无需对幻觉数据进行明确的训练.
- 评估模型对模拟振动的反应,以量化幻觉大小.
主要成果:
- 任务驱动模型成功地复制了经典的自感幻觉.
- 模拟幻觉的大小取决于模拟肌振动的频率.
- 模型表现显示了与上升的自感路径中的神经代码的相似之处.
结论:
- 单独的初级附带足以解释经典的自感错觉.
- 任务驱动模型为理解感官幻觉和身体状态估计提供了可行的框架.
- 这项研究为未来的理论驱动的实验调查奠定了基础.
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